The Decision Maker

The analytics research group has gathered thought leaders and industry experts to share its nine analytics predictions for 2014.

1. The focus shifts from individual analytics professionals to analytics teams.
This trend, which will manifest itself with analytics professionals embedded in business organizations or networked in a community or center of excellence, reflects the maturity of the analytics discipline. Greta Roberts, an IIA faculty member and the CEO at Talent Analytics, told us companies don't just have one guy doing analytics anymore; they have many. Now these people need to establish best-practices and work collaboratively on datasets and models, for example. Given what this means organizationally, "this is an awesome trend," said Bill Franks, IIA faculty member and chief analytics officer at Teradata.

2. Companies want to see value from big data in 2014.
In other words, they're over the hype. They want to see big data analytics tied to organizational goals, embedded in business processes, and leading to improvement and innovation. In essence, this is all about the Analytics 3.0 framework the IIA introduced this year, Franks said. Sanjeev Kumar, IIA faculty member and product manager at Dell Enterprise IT, called this a "pragmatic prediction, one that's very likely to come true for 2014."

3. Companies increasingly will use data analytics to create products and services.
Online companies like Google and LinkedIn have long been doing this, but many others will join in, said Tom Davenport, IIA research director and analytics author. This includes companies like General Electric, with all its sensor data, and software companies like Electronic Arts, which has online gaming opportunities to create.

4. Vendors and users will focus on operationalizing and managing models.
The key here, Franks said, is "getting analytics embedded right where business decisions are happening." Some of the analytics, in fact, will be self-updating and therefore will kick off business decisions. The trick is figuring out how to manage this process. "There's still a lot more to be done in this space," Kumar said.

5. The adoption of analytics-as-a-service will accelerate.
This is another easy one to predict, what with rising demand for specific types of analytics, the talent shortage, and the delivery of off-the-shelf and ready-made applications, said management consultant Bob Morison, another IIA faculty member. Kumar said, "What I like about this is that the potential for innovation is huge."

6. Predictive analytics will use facial recognition and wearable device data.
This may freak people out some, but there are a lot of innocuous uses, like optimized product placement based on knowing where customer eyes' move, Franks said. Roberts and Omer Sohail, IIA faculty member and principal with Deloitte Consulting, agreed this trend extends to the use of voice and speech, helping companies handle consumer complaints, improve customer experiences, and ultimately reduce churn.

7. Use of data visualization will increase for low- and high-complexity analytics.
Though data visualization does allow you to show trends, patterns, and movements in an effective way, Davenport had a cautionary note for those trying to use visualization in instances involving highly complex, multivariate statistics. That gets tricky, he said, because humans don't comprehend things in more than two dimensions or, at most, three dimensions.

8. Keeping pace with the speed and volume of data necessitates the continued move toward machine learning and automation.
Kumar said this is evidenced in the number of machine learning and artificial intelligence startups getting venture funding. Davenport added: "You can't handle big data without it."

9. Companies will focus on finding the optimal mix between humans and machines.
As theoretical as this might seem now, it is an important and growing issue for companies trying to operationalize analytics, Morison said. And Davenport said companies must absolutely be aware of the potential for unintended consequences arising from too much automation. "If you lose all the experts, who will develop the next generation of software?"

Which of these predictions seem spot on to you? How about those that seem too futuristic for a 2014 list? What about those that will be of most importance to your organization?